Methods and apparatus for coaching based on workout history and readiness/recovery information

ABSTRACT

System and method for coaching based on workout history and/or readiness/recovery information. Improved solutions enable intelligent management of a user&#39;s personal fitness journey based on workout recommendations that closely align with the user&#39;s traits. In one exemplary embodiment, workout data for a population of different individuals is analyzed to identify groups of similarly performing individuals. Each group of individuals is analyzed to generate an expected profile that approximates the physiological and/or psychological traits of the group. An expected profile includes heuristics and/or performance metrics that enable dynamic coaching during workouts. Subsequently thereafter, users can be dynamically coached by their client device, based on the expected profile.

PRIORITY

This application is a continuation-in-part and claims the benefit ofpriority to co-owned and co-pending U.S. patent application Ser. No.16/588,199, entitled “METHODS AND APPARATUS FOR COACHING BASED ONWORKOUT HISTORY”, filed Sep. 30, 2019, the contents of which areincorporated herein by reference in their entireties.

COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

TECHNICAL FIELD

This disclosure relates generally to the field of personal fitness. Moreparticularly, the present disclosure relates to systems, computerprograms, devices, and methods for coaching a user.

DESCRIPTION OF RELATED ART

In recent years, health and fitness tracking applications that trackuser workouts and activities have become very popular. Routine physicalactivity is important to a healthy lifestyle and is known to preventand/or ameliorate various health conditions, such as diabetes andobesity. Health and fitness tracking applications allow users to set andachieve personalized health goals by tracking the amount of physicalactivity, regularity of physical activity, and/or intensity of physicalactivity. These applications enable users to gain insights regardingtheir workout regimen efficacy.

However, existing health and fitness tracking applications often suggestworkout routines based on generalizations of the human body and/orassumptions about workout performance. Hence what is needed are improvedmethods for providing workout recommendations and coaching.

SUMMARY

The present disclosure addresses the foregoing needs by disclosing,inter alia, methods, devices, systems, and computer programs forcoaching a user based on workout history, readiness/recoveryinformation, and/or personal fitness goals, thereby providing improvedworkout recommendations and coaching.

In one aspect, a method for dynamically coaching a user based on one ormore readiness metrics is disclosed. In one embodiment, the methodincludes: monitoring the one or more readiness metrics; obtaining arecommended workout and a profile, the profile comprising a performancemetric; adjusting the performance metric based on the one or morereadiness metrics; monitoring a performance during the recommendedworkout; and when the performance does not match the adjustedperformance metric, providing dynamic feedback.

In one variant, the one or more readiness metrics comprises an amount orquality of sleep.

In one variant, the one or more readiness metrics comprises an estimatedcaloric availability.

In one variant, the one or more readiness metrics comprises a subjectiveinput provided by the user.

In one variant, the method further includes adjusting the recommendedworkout based on a start time, an end time, or a shortened availableduration.

In one variant, the dynamic feedback is additionally based on the one ormore readiness metrics.

In one variant, the method further includes updating a workout datarecord based on the one or more readiness metrics.

In one aspect, a method for providing a user with recovery coaching isdisclosed. In one embodiment, the method includes: obtaining arecommended workout and a profile comprising a performance metric;monitoring a performance during the recommended workout; when theperformance does not match the performance metric, providing dynamicfeedback; and updating a schedule based on the performance.

In one variant, the schedule includes a suggested rest event or asuggested post-workout consumption event.

In one variant, the updating includes modifying the suggested restevent, wherein the modified suggested rest event is based on theperformance.

In one variant, the updating includes modifying the suggestedpost-workout consumption event, wherein the modified suggestedpost-workout consumption event is based on the performance.

In one variant, the updating includes adding a suggested rest event or asuggested post-workout consumption event. In one such variant, themethod further includes creating a data record based on a completionstatus of the suggested rest event or the suggested post-workoutconsumption event. In one such variant, the updating the profile isbased on at least the data record.

In one aspect, a user apparatus is disclosed. In one embodiment, theuser apparatus includes: a user interface; a network interface; aprocessor; and a non-transitory computer-readable medium. In oneexemplary embodiment, the non-transitory computer-readable mediumincludes one or more instructions, which when executed by the processor,causes the user apparatus to: collect at least one of a readiness metricor a recovery metric; obtain a recommended workout and a profilecomprising a performance metric; and monitor performance during therecommended workout.

In one variant, the user apparatus includes a sleep tracking sensor andthe readiness metric includes an amount or quality of sleep measuredbefore the recommended workout.

In one variant, the user apparatus includes a sleep tracking sensor andthe recovery metric includes an actual amount or quality of sleepmeasured after the recommended workout.

In one variant, the one or more instructions, when executed by theprocessor, further cause the user apparatus to: log one or moreconsumption events; and estimate the readiness metric based on the oneor more consumption events before the recommended workout.

In one variant, the one or more instructions, when executed by theprocessor, further cause the user apparatus to: log one or moreconsumption events; and estimate the recovery metric based on the one ormore consumption events after the recommended workout.

In one variant, the one or more instructions, when executed by theprocessor, further cause the user apparatus to: solicit subjective userinput prior to the recommended workout; and estimate the readinessmetric based on the subjective user input.

More generally, various aspects of the present disclosure are directedto systems, apparatus, methods and storage media that coach a user basedon workout history, readiness/recovery information, and/or personalfitness goals.

Other features and advantages of the present disclosure will immediatelybe recognized by persons of ordinary skill in the art with reference tothe attached drawings and detailed description of exemplary embodimentsas given below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graphical representation of user assessment, in accordancewith the various principles described herein.

FIGS. 2A-2F are graphical representations of an exemplary workoutcoaching application, in accordance with the various principlesdescribed herein.

FIG. 3 is a logical block diagram of an exemplary network architectureconfigured to enable workout coaching based on workout history,readiness/recovery information, and/or personal fitness goals, inaccordance with the various principles described herein.

FIGS. 4A-4B are logical flow diagrams of methods for coaching a userbased on workout history, readiness/recovery information, and/orpersonal fitness goals in a health tracking system, in accordance withthe various principles described herein.

FIGS. 5A-5B are logical block diagrams of an exemplary server apparatusand health tracking devices useful in conjunction therewith, inaccordance with the various principles described herein.

FIGS. 6A-6F are graphical representations of one exemplary userinterface, consistent with the various principles described herein.

All Figures © Under Armour, Inc. 2019. All rights reserved.

DETAILED DESCRIPTION

Disclosed embodiments include systems, apparatus, methods and storagemedia which enable workout recommendations and coaching based on workouthistory, readiness/recovery information, and/or personal fitness goals.

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof wherein like numeralsdesignate like parts throughout, and in which is shown, by way ofillustration, embodiments that may be practiced. It is to be understoodthat other embodiments may be utilized, and structural or logicalchanges may be made without departing from the scope of the presentdisclosure. Therefore, the following detailed description is not to betaken in a limiting sense, and the scope of embodiments is defined bythe appended claims and their equivalents.

Aspects of the disclosure are disclosed in the accompanying description.Alternate embodiments of the present disclosure and their equivalentsmay be devised without departing from the spirit or scope of the presentdisclosure. It should be noted that any discussion herein regarding “oneembodiment”, “an embodiment”, “an exemplary embodiment”, and the likeindicate that the embodiment described may include a particular feature,structure, or characteristic, and that such particular feature,structure, or characteristic may not necessarily be included in everyembodiment. In addition, references to the foregoing do not necessarilycomprise a reference to the same embodiment. Finally, irrespective ofwhether it is explicitly described, one of ordinary skill in the artwould readily appreciate that each of the particular features,structures, or characteristics of the given embodiments may be utilizedin connection or combination with those of any other embodimentdiscussed herein.

Various operations may be described as multiple discrete actions oroperations in turn, in a manner that is most helpful in understandingthe claimed subject matter. However, the order of description should notbe construed as to imply that these operations are necessarily orderdependent. In particular, these operations may not be performed in theorder of presentation. Operations described may be performed in adifferent order than the described embodiment. Various additionaloperations may be performed and/or described operations may be omittedin additional embodiments.

For the purposes of the present disclosure, the phrase “A and/or B”means (A), (B), or (A and B). For the purposes of the presentdisclosure, the phrase “A, B, and/or C” means (A), (B), (C), (A and B),(A and C), (B and C), or (A, B and C). Similar logic applies to the useof the term “or” herein; i.e., “A or B” means (A), (B), or (A and B).

The terms “comprising,” “including,” “having,” and the like, as usedwith respect to embodiments of the present disclosure, are synonymous.

Workout Routines, Rest and Recovery, and Human Physiology and Psychology

Most holistic health and fitness goals include some aspect of strengthtraining and cardiovascular exercise. Similarly, many athleticactivities (recreational, amateur, and/or professional) requiresignificant strength and cardiovascular exertion. Thus, workout routinesare generally categorized into cardiovascular workouts (e.g., walking,running, swimming, biking, etc.) and strength workouts (e.g.,weightlifting, sprints, etc.) Some hybridized workout regimens mix bothcardiovascular and strength training.

As a brief aside, the human body's cardiovascular system circulatesoxygen and removes waste products from the skeletal muscles by pumpingblood. The cardiovascular system relies on a unique type of cardiacmuscle fiber to accomplish this task; cardiac muscle fiber is aninvoluntarily controlled muscle that is highly specialized and foundnowhere else in the human body. The cardiovascular system's performanceremains remarkably consistent over prolonged exertion.

In contrast to cardiac muscles, skeletal muscles are part of the humanskeletal system (e.g., muscles, bones, tendons, ligaments, etc.)Skeletal muscles fatigue as a function of duration and intensity of use;their performance can be highly dynamic depending on composition. Modernresearch has shown that skeletal muscles are 3-dimensional structures ofvarying muscle fibers and connective tissue. Human muscle fibers aregenerally classified as “slow twitch” and “fast twitch”, however musclefiber types exist on a continuum and can express both slow and fasttwitch qualities. The structure of skeletal muscles widely varies amongdifferent people; for example, some people may have muscle compositionsthat can easily provide bursts of activity at high load, others may havemuscle compositions that provide sustained endurance at lower loads,etc.

Humans widely vary in their baseline physiology and psychology; thesedifferences affect workout efficacy; for example, two people ofdifferent height, weight, fitness, musculature, and mental outlook mayrun 5 miles or lift weights with different levels of exertion andperformance progression. As a practical matter, individual differencescan introduce problems for people that exercise with a particular goalin mind.

For instance, most strength training routines are static and/orinflexible e.g., “20 kettle bell swings, 15 pounds”, “15 bicep curls at80% of maximum.” Similarly, since the cardiovascular system isinvoluntary, cardiovascular workouts only indirectly exercise themuscles based on physical activity. Most endurance workouts prescribe aset time, set distance, etc. (e.g., “run 5 miles”, “bike for 30minutes.”) Typically, these static workout routines assume a particularmusculature and/or fitness level; there is no way to dynamically adjustthe workout routine to suit individual needs. For example, a person thathas suffered an injury may need to change a workout routine to preventfurther injury. In another such example, a person may select a workoutfor 45 minutes during a lunch break but end up finishing too early (ortake too long). In yet another example, a person may not be able to userequired equipment (e.g., a specialized machine may be too busy duringcertain gym hours.) Even if the person has completed a modified workout,they may incorrectly log the workout data in their health and fitnesstracking applications (e.g., over/under report activity, etc.)

As but another example, athletes often tailor their workout regime so asto improve performance for a particular athletic motion (e.g., throwinga ball, etc.) Many athletic activities incorporate multiple musclegroups working in concert (e.g., throwing a ball requires the arms,chest, back, stomach, and leg muscles to fluidly coordinate.) Incontrast, most workout techniques and weightlifting equipment arefocused on a single (or few) muscle group(s). While a professionalathlete may be closely scrutinized by physical therapists and prescribedspecific muscle group workouts to tune their performance, amateur and/orcasual hobbyists seldom have access to the same resources. Instead theymay resort to imperfectly copying workouts for lack of betteralternatives. Unfortunately, this may result in widely divergingperformance gains merely because they've over/under emphasized certainmuscle groups.

As a related note, individual behaviors and motivations span a widerange. Some individuals are highly goal oriented in their workoutregimen; other individuals may enjoy working out to “blow off steam.”Some individuals may work through pain, even to their own detriment;others may need encouragement when a workout is difficult (but stilldoable). As a practical matter, these “intangible” factors areimpossible to capture in static workout recommendations. Currently,dynamic feedback is only possible in the form of a human personaltrainer/coach that actively observes an individual's workout;unfortunately, hiring a personal trainer can be cost-prohibitive formost gym goers.

Certain companies and/or gyms regularly publish workout routines viae.g., applications and websites, blogs, and other media sources, toboost gym attendance and/or attract new subscribers. While theseservices may provide well-intentioned information and/or generallyacceptable guidance, there are no cost-effective solutions that areindividually tailored. Furthermore, individuals with specific goals maybe well aware of their own limitations and/or idiosyncrasies, and yet beunable to find workout routines that are germane to their personalfitness journey.

As but another complication, while common sense and empirical evidencesuggests that sleep and nutrition are important for pre-workoutreadiness and post-workout recovery, most workout regimens are focusedon the workout itself (e.g., sets, reps, heart rate, etc.) The interplaybetween sleep, nutrition, and workout performance is largelyunresearched and/or poorly characterized.

Notably, existing sleep and/or nutrition tracking is primarily focusedon long term behaviors and effects. For example, sleep tracking has beenused in medical treatment for chronic sleeping issues (e.g., insomnia,oversleeping, etc.). Even though many health tracking devices canmonitor a user's sleep on a day-to-day basis, existing devices do notaddress the user-specific short term (e.g., next day) effects of sleepdeprivation on performance.

Similarly, most consumers use meal tracking for weight loss. However,weight loss is a gradual process that takes weeks (if not months or evenyears) to see consistent results. Existing meal tracking applications donot address the immediate effects of consumption; e.g., meal trackingapplications do not plan meals around user-specific blood sugarmetabolism and/or caloric requirements for upcoming activities.

Non-workout behaviors may greatly impact the user's instantaneousworkout “readiness” and/or “recovery”. Unfortunately, as with otherphysiological and/or psychological aspects, humans widely vary in theirrequired sleep and/or nutrition. For example, even though a poorlyrested user is likely to be undermotivated and underperform; “poor rest”can widely vary across the population (e.g., 7 hours of sleep may be toomuch for one user, but more than enough for another). Similarly, failingto refuel with adequate macronutrients (e.g., protein, carbohydratesand/or fats) can result in suboptimal muscle recovery. However,different people metabolize food at different rates; a protein shake maywork for one user but be wholly inadequate for another. More directly,existing sleep and nutrition techniques are poorly suited for dynamicworkout coaching.

In view of the existing health and fitness ecosystem, improved solutionsare needed to enable consumers to intelligently navigate their personalfitness journey. There exists a persistent need to provide efficient andeasy-to-use mechanisms for getting relevant workout recommendations andcoaching.

Example Operation

The exemplary solution described herein addresses the foregoing byproviding workout routine coaching based on e.g., workout history,readiness/recovery information, and/or personal fitness goals. Aspreviously alluded to, the wide variation in human physiology andpsychology cannot be adequately addressed by static workout routines,one-size-fits all readiness and recovery guidance, and fixed workoutprogressions. However, an individual's performance can be predictedbased on comparisons to groups of similar individuals. For example,power lifters have (or intend to develop) muscle performance similar toother power lifters. Likewise, endurance athletes have (or intend todevelop) muscle performance similar to other endurance athletes. “Earlybirds” may benefit from different workout schedules than “night owls”;similarly, a user on diet restriction may require specializedpre-workout/post-workout meals.

In one exemplary aspect, the workout and readiness/recovery history forthe population of users is analyzed to create one or more expectedprofiles. Each expected profile is associated with the heuristics and/orperformance metrics for a subset of users having similar physiology andpsychology. In one exemplary embodiment, the expected profiles aregenerated based on artificial intelligence (AI) and/or machine learningto identify similar users and their corresponding heuristics, rules,and/or patterns. While the exemplary embodiment is based oncomputer-assisted data science and data analysis techniques, otherimplementations may use human assessments (e.g., fitness experts,coaches, etc.) or some hybrid of human and machine learning. Asdescribed in greater detail infra, the initial expected profiles may becorrected and improved gradually over time as more and bettercrowd-sourced workout history is collected.

In one exemplary embodiment, a user may run through an initialassessment to grossly identify their “baseline” physiology and/orpsychology. For example, establishing a baseline via an initialassessment may include one or more sets of squats, lunges, sit-ups,push-ups, pull-ups, abdominal rotations, a timed run, and/or aquestionnaire (e.g., “how did you feel?”, etc.) Completing the initialassessment provides baseline information as to the person's physiology(e.g., strength/stamina for each of the major muscle groups and/orcardiovascular endurance) and/or psychology (e.g., confidence,motivation, emotional state, etc.) In some cases, a user may be requiredto meet a minimum threshold of physiological and/or psychologicalperformance for the assessment to be accurate. For example, a user thathas never logged any workouts may be instructed to first establish aregular workout regimen before attempting assessment (e.g., “get in thehabit of walking every day” before “run a timed mile.”) Common metricsuseful for generating a baseline or initial assessment may include,without limitation e.g., distance for an exercise (run, walk, bike,swim, etc.), repetitions accumulated, a particular pace thresholdmaintained for a specific duration, a minimum/maximum load, and/or anyother physiological or psychometric metric.

In one embodiment, readiness and/or recovery metrics may be collectedfor an assessment period, obtained from historic tracking data, orinferred from existing schedule data. For example, nutrition informationmay be calculated based on meals that are logged by the user. Similarly,sleep tracking metrics may be retrieved via application programminginterface (API) calls from a sleep tracking application. Still otherimplementations may infer sleep and/or nutrition based on the user'sintended schedule and forecasted meal plan (e.g., 8 hours of sleep/day,2000 calories/day).

Subsequently thereafter, the user may continuously update and/or augmentinput on their personal fitness goals and continue to track theirworkout history, sleep, nutrition and/or any other performance relateddata. Their baseline assessment, personal fitness goals, and/or ongoingworkout history can be used to identify a matching expected profile.Subsequent workouts and/or other day-to-day activities can berecommended to the user based on the matching expected profile.Similarly, during a workout or other day-to-day activity, the user canreceive dynamic feedback based on the expected profile; e.g., the usercan be immediately notified and coached when they are over/underperforming. In another such example, the user can receive dynamicfeedback when they are staying past their optimal bedtime, skipping ameal, etc. In some cases, the user may be able to take correctiveactions and/or accurately log their progress.

FIG. 1 is a graphical representation of user assessment (benchmarking),in accordance with the various principles described herein. As shown inFIG. 1, workout history is collected from a large population of usersfor an exercise (e.g., sit-ups, push-ups, squats, etc.) The workouthistories are analyzed to generate a set of expected profiles. In thissimple example, the expected profiles are shown in a two-dimensional“bubble chart” of maximum repetitions and maximum sets for an assessmentexercise. More complicated embodiments may use a higher dimensionalmulti-variate analysis, the example of FIG. 1 being purely illustrative.Examples of variables that may be useful in a multi-variate analysis mayinclude without limitation: age, sex, ethnicity, training load, trainingfrequency, height, weight, sleeping habits, eating habits, and/or anynumber of other physiological and/or psychological parameters (e.g.,behaviors, motivations, etc.).

Initially, the user benchmarks their performance in the assessmentexercise. The user's performance is compared to the expected profiles;in this case, the user's performance 102A most closely matches expectedprofile: “profile 1”. Profile 1 identifies the appropriate heuristicsand/or performance metrics suitable for a subset of peers having similare.g., physiology, psychology, and fitness goals. Notably however, as theuser continues to regularly exercise and improve, their associatedexpected profile may change. For example, the user's performance metricschange over time (e.g., from performance 102A to performance 102B) andare associated with different expected profiles (e.g., profile 5,profile 6, . . . profile 14), these changes in expected profiles reflectthe user's own personal fitness journey. Unlike conventional solutionsthat try to put every individual on a one-size-fits-all workoutprogression, the user's personal fitness journey changes the coachingand recommendations based on expected profiles that are most relevant totheir current abilities and motivations.

FIG. 2A illustrates one exemplary personalized workout recommendationand dynamic feedback user interface 200 based on workout history,readiness/recovery information, and/or personal fitness goals,consistent with the various principles described herein. As showntherein, the personalized workout recommendations are based on theheuristics and/or performance metrics associated with the expectedprofile and may be modified to incorporate readiness/recoveryinformation. For example, the illustrated interface 200 recommends a setof exercises (e.g., “Sit-ups” 202A, “Push-ups” 202B, “Squats” 202C,etc.) and logs actual performance (204A, 204B, 204C) against an expectedgoal performance for the expected profile (206A, 206B, 206C). The userinterface 200 may further include status e.g., a set count and/ormotivational messaging (“New Personal Record!” 208).

A user may navigate the workout (“PREV”, “NEXT”) with virtual buttons.In the illustrated example, a user may also scroll through the workoutusing a horizontal user interface (UI) gesture (swipe back/forth) toselect between activities. In some cases, the recommended workout mayalso provide the user with instructional information (e.g., exercisedmuscle group, proper form, why the exercise is important, etc.) In someembodiments, the interface 200 may be configurable based on userpreferences. For example, a user may prefer vertical swiping in a“portrait” orientation (or change to a “landscape” orientation).Similarly, a user may prioritize, re-order, add, skip, start/stoptimers, and/or remove workout recommendations. In some cases, a user mayconfigure how workouts are displayed; for example, a user may prefer tosee more or less detailed information e.g., bigger/smaller images,repetitions, weights, sets, distance, time, etc. Other variations of theinterface 200 may be used with equal success.

Additionally, the exemplary user interface 200 can provide dynamicfeedback 210 in response to such user input and/or during the workout.In some cases, dynamic feedback may be provided in response to skippingan exercise (e.g., “Why did you skip squats?”). In another example, asshown in FIG. 2A, a user that has failed to meet the expectedperformance may receive a notification (described in greater detail inFIGS. 2B-2C infra). Dynamic feedback may include a variety of differentmessages e.g.: interrogatory (e.g., “You have only completed 18 of 25push-ups. How do you feel?), suggestions (e.g., “For this set, let'sfocus on form”), motivation (e.g., “You're doing great!”), information(e.g., “Push-ups in a wide stance or a narrow stance can activatedifferent muscle groups”), status (“Only one more set to go”), etc.

While the illustrated embodiment uses visual feedback, otherimplementations may provide e.g., audible (voice, music, etc.) and/orhaptic (vibration, etc.) feedback. Voice instruction, feedback, and/orcommands may be useful where the user cannot directly touch the fitnesstracking device. For example, during a push-up, the user's device mayinstruct the user to start a push-up with an audible “Down”; the usermay respond with an audible “Up” to indicate the completion of arepetition. The user device may additionally provide feedback e.g.,“That was too fast; for the next rep, slow down”, etc. Similarly, hapticfeedback may be useful for certain applications where the user can feelthe device but need not directly respond (e.g., a smart watch may senseheart rate, and vibrate once a target heart rate has been reached, etc.)

Referring now to FIG. 2B, one exemplary implementation of a dynamicfeedback interface 220 is provided in greater detail. As shown therein,a user has failed to meet the expected performance. In the illustratedembodiment, the user device prompts the user to provide user input 210to explain why. For example, a user may select a subjective emotionalstate (happy, indifferent, afraid of injury). In other cases, user inputmay be objective (e.g., injury, pain, inability, equipmentunavailability, etc.) Still other types of user input may be provided,the foregoing being purely illustrative.

In response to user input, the user device adjusts the workout inaccordance with the heuristics associated with the identified expectedprofile (revised interface 230 of FIG. 2C). In the example shown in FIG.2C, the expected profile heuristics determine that the user's failure tomeet expected performance is indicative of an increased risk ofpotential injury. Specifically, other users that are similar to thisuser and exercising under similar circumstances, are at increased riskof injury. Consequently, the workout is revised to an expectedperformance 206D such that the user can continue with a reduced goal; aninformational note “Avoid Injury, Focus on Form” is also provided.

Notably, the heuristics for different expected profiles may vary widely;for example, a different heuristic could interpret the user's input asbeing indicative of lack of motivation. In response, the user device mayoffer more encouragement and/or suggest other more enjoyable activities.Still other heuristics may adjust subsequent workout stages so as tocomply with a user's limitations; for example, a user that is takinglonger than expected to finish the Push-up stage may be instructed to doa shorter run later on (so as to stay within a prescribed total workouttime).

Once the user has completed the prescribed workout, the workout islogged appropriately (e.g., that the user revised the goal due topotential injury, and that the revised goal was met). In some cases,user input can be used to continuously improve the correspondingexpected profiles and/or better match the user's performance with otherexpected profiles. In particular, users associated with an expectedprofile are treated similarly (e.g., prescribed similar workouts)because they are inferred to have similar physiologies and/orpsychology. Performance progression for similar individuals results inpositive feedback (further reinforcing the expected profile accuracy)whereas differences are negatively correlated (pushing users towardbetter fitting expected profiles). In other words, improvement to theuser's classification and the expected profiles may be an emergentproperty of continuous user workout history analysis.

As previously alluded to, a user's schedule can affect workoutperformance in at least two (2) independent and distinct ways. Firstly,unlike physiological parameters which the user does not control (e.g.,age, sex, height, weight, ethnicity, etc.) the user's day-to-day choicesreflect their own agency. A user's behavior may be indicative of e.g.,diligence, intensity, motivation, discipline, and/or other psychologicalstates. For example, consistent detailed meal logging is characteristicof a much different psychological profile than inconsistent incompletelogging. Similarly, a user that regularly sleeps at 10 PM and wakes at 6AM is likely much different in behavior than an irregular sleeper.Consequently, user scheduling information can be incorporated within theaforementioned expected profiles as part of the psychologicalparameters.

Additionally, the user's day-to-day choices directly impact theirshort-term performance. A user that does not adequately rest and re-fuelwill have sub-optimal workouts and slower performance progression. Tothese ends, various embodiments of the present disclosure are directedto a holistic “program” that includes not only workout routine coaching(such as described supra), but also non-workout coaching. More directly,the expected profile can be extended to non-workout recommendationsand/or dynamic feedback throughout their other day-to-day activities.The exemplary holistic program maximizes pre-workout readiness, enablesdynamic coaching during workout routines, and/or intelligently managespost-workout recovery.

FIG. 2D illustrates one exemplary personalized fitness program userinterface 240 based on workout history, readiness/recovery information,and/or personal fitness goals, consistent with the various principlesdescribed herein. As shown therein, the exemplary program viewintegrates with a user's existing calendar applications executed fromtheir e.g., smart phone. The program view provides both recommendedactionable items for activities in the future, and an actual record oflogged actions for activities in the past (relative to the current time242.) For example, a user may browse through their calendar to see howwell they slept 244 (e.g., what percentage of their sleep was restlessversus deep sleep), what they've eaten 246, and their upcomingrecommended workout 248.

In one embodiment, the fitness program fits workout routines within theuser's existing scheduling based on a complete picture of the user'scurrent health (previous workouts, sleep, nutrition, etc.) and theuser's associated expected profile. Readiness and recovery coachingbefore and after the workout ensure that the user is rested and refueledto receive the workout's maximum benefit (thereby speeding performanceprogression). As a practical matter, many users will encountersituations where they cannot get sufficient rest/nutrition; under thesecircumstances, the workouts can be modified to compensate for thesedeficiencies. In other words, the user may benefit more from completinga scaled back workout that accounts for their current physiologicaland/or psychological state than e.g., skipping the workout, orattempting an unrealistic workout.

As previously alluded to, the fitness program may incorporateinformation from a number of different sources. For example, a user mayuse a sleep tracking device to track their daily sleep cycle, a smartwatch and/or smart shoe to track their workouts, and a smart phone toplan and log meals. Workout, sleep, and/or meal tracking data can bedirectly retrieved from the devices, and/or indirectly obtained e.g.,access via 3^(rd) party application programming interfaces (APIs). Forinstance, a sleep tracking device, smart watch, and/or smart shoe canprovide data to a smart phone via health tracking APIs; the data may bemade locally available to any appropriately permissioned health trackingapplication of the smart phone. Similarly, a meal tracking blog mayprovide external web portal interfaces to enable recipe query andnutrition information retrieval.

In other implementations, the fitness program may rely on user input(e.g., where the user manually logs their activities.) In some suchvariants, the fitness program may automatically populate meal, rest,and/or workout data records with recommended values that the user eitherconfirms, rejects, and/or modifies. For instance, a sleep record may bepopulated with the recommended 8 hours of sleep from 11 PM to 7 AM; theuser may accept the default sleep record or update the sleep record withtheir best estimate.

In one embodiment, the fitness program is seamlessly incorporated withinthe user's daily schedule for day-to-day activities. For example, theuser's calendaring program may include a variety of personal and/orprofessional appointments as well as reminders or links for health andfitness activities (e.g., suggested meals/snacks, rest reminders,suggested workouts, etc.) In some cases, the calendaring application maydirectly launch the corresponding tracking application. For instance, auser may browse to their suggested meal and automatically launch theirmeal logging application to conveniently log their actual mealconsumption. Similarly, a user may receive a sleep summary; if the useris interested, they can directly launch the sleep application to viewtheir sleep summary.

The holistic program enables dynamic modifications to workouts based oncurrent user readiness metrics. For example, as shown in the exemplarypersonalized fitness program user interface 250 of FIG. 2E, at time 252,the user has skipped the suggested lunch (skipped meal event 256). Oncethe user decides to start their 1PM workout, the workout 258 isdynamically modified to account for the skipped lunch event. In anothersuch implementation, the fitness program may consider subjectivereporting measures of readiness. For example, the user may be promptedto answer questions regarding e.g., soreness, energy level, mood, and/orsleep. Modifying the workout to account for reduced user readiness ismore beneficial than e.g., skipping the workout or attempting anunrealistic workout (and risking injury).

In some embodiments, the user's actual sleep and/or nutrition data maybe used to estimate e.g., muscle recovery, response times, awareness,blood sugar, estimated caloric availability/deficiency,hydration/re-hydration, etc. Specifically, the user's workout may bemodified such that a population of similarly situated individuals(physiological and psychologically related peers) could reasonably beexpected to derive benefit therefrom.

As but another such example, the user's calendar data and/or locationdata can be used to make trade-offs where necessary. For example, if theuser is starting their workout late and has a hard stop, then theworkout can be adjusted such that the user's most valued exercises(exercises that are most aligned with the user's goals) are prioritizedand can be completed. Similarly, earlier start times (e.g., a longerworkout duration) can be used to pack more exercises into the same timeslot. Other considerations may include e.g., available equipment, nearbyworkout partners, commonly attended classes, etc.

The holistic program also enables dynamic recovery coaching afterworkouts based on actual workout performance. For example, as shown inthe exemplary personalized fitness program user interface 260 of FIG.2F, at time 262, the user has skipped lunch and completed a modifiedworkout. Unfortunately, the user has dropped into a caloric deficiencydanger zone (as illustrated by the graph of caloric consumption overtime 265). Caloric consumption may be estimated based on the actualworkout metrics; more sophisticated variants may incorporate e.g., theuser's physiology and metabolism.

The caloric deficiency event triggers an update to dynamic recoverycoaching; responsively, the user is notified to consume their afternoonsnack earlier. In some cases, the snack itself may be modified toprovide e.g., more calories, a specific blend of macronutrients, etc.For example, the suggested refuel snack may be a much more substantialsmoothie 266 (rather than a handful of nuts, etc.) When the userconsumes the refuel smoothie, the caloric consumption graph is updated(the user exits the caloric deficiency danger zone 267). Alternatively,if the user does not consume a refueling snack (or refuels with a lesssubstantial snack), then the user's recovery may be impacted. In somevariants, the severity of impact may be estimated based on the expectedprofile modeling.

More generally, the disclosed embodiments of the present disclosureensure that users receive instantaneous, accurate, and targeted feedbackduring workouts and/or other day-to-day activities which can greatlyimprove the performance progression. The disclosed solutions enableusers to conveniently navigate their personal fitness journey. Theforegoing discussion of the exemplary implementation is purelyillustrative; artisans of ordinary skill in the related arts may add,remove, and/or substitute similar functionality, given the contents ofthe present disclosure.

Network Architecture

As previously alluded to, existing solutions have not addressed theholistic health and fitness of users. Consequently, different aspects ofhealth and fitness have been historically isolated from one another inthe consumer electronics space. For instance, sleep data, meal data,and/or workout data may be “silo-ed” into different consumerapplications and/or device ecosystems. To these ends, variousembodiments of the present disclosure aggregate health data from a broadvariety of different sources (including 3^(rd) party sources and/orlegacy databases).

Referring now to FIG. 3, an exemplary network architecture 300configured to enable workout coaching based on workout history and/orreadiness/recovery information is shown. As illustrated, the system 300includes one or more user devices 302 in communication with a healthtracking network 304. In one exemplary embodiment, the health trackingnetwork 304 may include one or more of analytics engines 306 incommunication with a user workout history database 308, sleep trackingdatabase 310, meal tracking database 312, and a database of expectedprofiles 314.

The health tracking network 304 may include one or more wired and/orwireless, private and/or public network, including but not limited to,e.g., the Internet. The health tracking network 304 is, for example, awireless local area network (WLAN), wireless wide area network (WWAN),wired network, or any other suitable communication channel. Accordingly,each of the user devices 302, analytics engine 306, and databases (e.g.,user workout history database 308, sleep tracking database 310, mealtracking database 312, and expected profiles database 314) areconfigured with appropriate networking communication interfaces. Anexample of wired communication interface may include, but is not limitedto, Ethernet; while examples of wireless communication interfaces mayinclude, but are not limited to, near field communication (NFC),Bluetooth, Wi-Fi, 4G or 5G LTE. It is further appreciated that variousgateways, routers, switches, base stations, and so forth may be involvedin facilitating and forwarding communication between the foregoingdevices. Additionally, it is noted that the foregoing health trackingnetwork 304 may be itself, composed of several networks, such that thedescribed components are distributed in various ones thereof. Inalternative embodiments, the health tracking network 304 may include aseries of devices communicating within software via software API's.

As used herein, the term “database” refers to a structured set of datarecords held within a non-transitory computer-readable medium and/or themechanisms used to e.g., add, remove, modify, and/or query and retrievethe stored data records. The term “data record” refers to a collectionof data structures that represent an association, grouping,organization, or other collection of information; common examples ofdata structures include without limitation: numbers (integers, floatingpoint), values (Booleans, enumerations), characters, strings, arrays(1D, 2D, N×D, etc.), lists, hash tables, etc. For example, a databasemay be queried for one or more data records that satisfy a particularcondition; e.g., containing a particular string, value, etc.

The user workout history database 308 stores a plurality of user datarecords and their corresponding workout data records. Each user datarecord may include detailed information with regard to e.g., accuracy ofdata, fitness goal definition, progression of performance, psychologicalparameters (e.g., behaviors, motivations, etc.), height, weight, age,sex, ethnicity, and/or any number of other user specific parameters.Each workout data record may include detailed information with regard toe.g., date/time of past exercises, scheduled date/time of futureexercises, type and/or number of exercises, frequency of exercise,exerted muscle groups, duration of exertion, intensity of exertion,absolute load, relative load, range of movement, repetition, recoverytime, fatigue, dynamic feedback/user response, frequency of revision,revision success/failure, and/or any number of other workout specificparameters. More generally, artisans of ordinary skill in the relatedarts given the contents of the present disclosure, will readilyappreciate that virtually any data regarding either the individual usersand/or their specific workout history can be stored.

The user sleep tracking database 310 stores user histories of sleep assleep data records. Each sleep data record may include information withregard to e.g., date/time of sleep, suggested sleep time, actual sleeptime, contiguity of sleep, irregularity of sleep, scheduled date/time offuture sleep, quality of sleep (e.g., the clinical definition for sleepquality is based on the number and/or length of waking events),subjective user data (e.g., tiredness, time to alertness, etc.), and/orany number of other user rest parameters. As used herein, the term“sleep” generally refers to any rest event (e.g., naps, mid-day“siestas”, etc.)

The user meal tracking database 312 stores user histories of consumptionas consumable item data records and/or their constituent ingredients.Each consumable item data record may include information with regard toe.g., date/time of consumption, the suggested consumable and usermodifications thereto, actual consumption, portion size, time intervalover which the consumable was consumed (e.g., to estimate time varyingeffects), nutrient-related information, subjective user data (e.g.,enjoyment, satisfaction, etc.), and/or any number of other userconsumption parameters.

Common examples of nutrition-related information may includemacronutrient information (e.g., calories, protein, fat, carbohydrates),micronutrient information (e.g., water-soluble vitamins, fat-solublevitamins, minerals, fiber, water, etc.), allergen/intoleranceinformation (e.g., lactose, gluten, peanut, etc.), ingredient type,ingredient size, portion size, and/or other food-related information.Consumable items may include single ingredient items (such as meat,fruit, vegetables, grains, etc.) as well as multi-ingredient items(e.g., recipes, menu items, meals, etc.). As used herein, the term“consumable” generally refers to foods, beverages, and other consumablessuch as vitamins, supplements, medications, etc.

The expected profiles database 314 stores heuristics and/or performancemetrics for one or more expected profiles. For example, an expectedprofile may define a muscle group (e.g., abdominals), an associatedexercise (e.g., sit-ups), and a workout with a series of expectedperformances and acceptable ranges (e.g., four (4) sets of increasingrepetitions (25, 30, 35, 40) plus or minus 1 set, 5 sit-ups). Users thatare associated with the expected profile should be able to meet theexpected performances. Actual performance that deviates from theexpected performance beyond an acceptable range may trigger execution ofa heuristic and/or feedback. For example, a user that exceeds theirexpected performance range (e.g., performing 50 sit-ups where only 35-45sit-ups were expected) may receive congratulatory feedback, a user thatunderperforms the expected performance range may be encouraged toimprove and/or cause modification to subsequent workouts. In some cases,the heuristics may take user input into account, for example, a userthat reports positively (e.g., “I've almost got it, let me try again”)may have a smaller correction than a user that subjectively reportsnegatively (e.g., “I think that's all I got”).

In one embodiment, the expected profiles database 314 additionallyincludes heuristics and/or performance metrics that enable modificationsbased on readiness. Modification rules may e.g., scale back or adjustacceptable tolerances based on certain conditions. For example, insteadof requiring a sleep deprived user to do 4 sets, their workout may bescaled down to 3 sets. Similarly, a user that would normally have atolerance of plus/minus 5 repetitions may be allowed a range ofplus/minus 10 repetitions when they've skipped a meal. Other methods forincreasing or decreasing workout intensity may include changing e.g.,sets, repetitions, duration, rest intervals.

In some variants, the heuristics and/or performance metrics may accountfor differences in workout performance attributable to readiness (orlack thereof). For example, data analytics performed over a set ofsimilarly situated and motivated users under varied readiness conditions(e.g., sleep deprived, underfed, etc.) may be used to generate expectedprofiles. Such profiles may be used to predict how the user will performunder analogous conditions and/or modify workouts accordingly.

In another embodiment, the expected profiles database 314 may includeheuristics for post-workout recovery coaching. In some cases, recoverycoaching may use actual logged workout performance to update the user'srecovery program. As but one such example, a user that has had aparticularly strenuous workout may be coached to consume a substantialpost-workout snack. Similarly, rest and recovery messaging may beprovided (e.g., “good job, remember to rest and refuel”, etc.)

As previously noted, different human physiologies respond to rest and/ornutrition differently. Consequently, users with different fitness goalsmay be better served with different recovery strategies. The expectedprofile may describe optimal recovery strategies for the user (asdetermined by similarly situated peers). For example, a power lifter mayneed much more protein post-workout than an endurance athlete.Similarly, a performance athlete may be willing to eke out every ounceof gain using precise ramp-up preparation and taper down strategiesbefore significant competition events.

It is appreciated that in the illustrated embodiment, the aforementioneddatabases (308, 310, 312, 314) are separate and distinct from theanalytics engine 306 and/or user device(s) 302. However, in othervariants, the databases may be incorporated in part or in whole witheither the analytics engine 306 and/or the user device(s) 302 forstorage thereat. For example, workout data records that have been logged(or scheduled) at a particular user device 302 may be stored locallyuntil e.g., synchronized with the network (or vice versa). Additionally,or in the alternative, expected profiles (in whole or in part) may bestored at the analytics engine 306 and portions may be made accessibleto particular devices 302 when queried and/or locally cached. Anycombination of the foregoing configurations may be utilized with equalsuccess.

While the foregoing example is presented in the context of strengthtraining/calisthenic/cardiovascular type routines, artisans of ordinaryskill in the related arts will readily appreciate that the variousprinciples described herein may be readily adapted to virtually anyactivity e.g., sports activities, academics, hobbies, etc.

Methods

FIG. 4A is a logical flow diagram of an exemplary method 400 forproviding workout routines to a user based on workout history and/orreadiness/recovery information, in accordance with the variousprinciples described herein.

At step 402 of the method 400, a health tracking system analyzes workoutdata and/or readiness/recovery information to generate one or moreprofiles. In one embodiment, workout data and/or readiness/recoveryinformation for a large population of users is analyzed to classifyusers into “groups” that are physiologically and/or psychologicallysimilar. In one exemplary embodiment, the identified “group” is analyzedto create an expected profile that parameterizes heuristics and/orperformance metrics for its constituent members.

In one specific implementation, readiness/recovery information isanalyzed to identify the short-term and/or long-term effects onindividual performance. “Short-term”, as used in reference to readinessand recovery herein, refer to time varying effects caused byphysiological and/or psychological limitations of an individual. Incontrast, “long-term” refers to the average capabilities and behaviorsfor an individual (the central tendency of a distribution). For example,different human physiologies metabolize nutrients at different rates.Even though two humans may both consume 50 grams of carbohydrates, theshort-term spike in blood sugar is based on individual metabolism (whichmay vary widely based on human physiology). In contrast, the totalnumber of calories consumed is an average property of human physiology(e.g., the metabolized calories distribution is centered around 450calories). While long-term sleep deprivation and malnutrition certainlycause chronic health issues (where the performance of an individualdecreases on average), these are fundamentally different from large (buttemporary) variances in overall workout performance that may beattributed to short-term effects of insufficient rest and/or nutrition.

As used herein, the term “readiness” refers to user activities thatoccur prior to a workout that affect the user's short-term workoutperformance. For example, sufficient rest and nutrition immediatelyprior to a workout can maximize a user's motivation and caloricavailability. In contrast, the term “recovery” refers to user activitiesthat occur after a workout that affect the user's long-term performanceprogression. For example, habitually resting and refueling after aworkout can improve muscle growth and metabolism trends over time.

While various embodiments of the present disclosure are discussed in thecontext of rest and nutrition (e.g., to optimize readiness andrecovery), artisans of ordinary skill in the related arts given thecontents of the present disclosure will readily appreciate that othernon-workout activities may affect readiness and/or recovery, theforegoing being purely illustrative. Common examples of such non-workoutactivities include without limitation: hydration, meditation, mentalpreparation (e.g., team play reviews), circadian rhythms, mental and/orphysical stressors (alcohol/caffeine/nicotine consumption, long workhours, etc.), hobbies, and/or other personal interests.

In another embodiment, workout data and/or readiness/recoveryinformation for specific individuals is analyzed to identifyphysiological and/or psychological traits for that individual. Forexample, celebrities and/or athletes may have their own workout dataand/or readiness/recovery information analyzed. The celebrity and/orathlete may then publish their workout data and/or readiness/recoveryinformation such that other individuals can aspire to holistically steertheir workouts and/or readiness/recovery habits to match thecelebrity/athlete. More directly, even though a professional athlete mayfocus on strength training for specific muscle groups, an amateur mayactually need to improve many different muscle groups (and/or when andhow they eat/sleep) to achieve similar results. In other words, thecelebrity profile can be used to dynamically coach a person toward anaspirational level of fitness of another person.

In still another embodiment, workout data and/or readiness/recoveryinformation for a specific group of individuals may be analyzed. Forexample, an athlete may want to tailor their workouts and/orreadiness/recovery habits to match against a peer set of specificathletes, e.g., a running back for a football team may want to know howthey compare against all running backs in the league. Still othervariants of the foregoing may be substituted with equal success, byartisans of ordinary skill in the related arts.

Referring back to step 402, the workout data and/or readiness/recoveryinformation analysis can be performed via machine learning and/or otherartificial intelligence (AI) techniques. Specifically, machine learningand/or artificial intelligence (AI) can be used to filter populations ofusers into subsets having similar characteristics. Each subset isassociated with appropriate performance metrics based on e.g., workoutand/or readiness/recovery history, interpolation/extrapolation fromworkout data, interpolation/extrapolation based on related muscle groupexercises and workouts (e.g., pull-ups may enable some extrapolation tosimilar muscle exercises e.g., bicep curls, etc.) and/or userinput/responses. In other implementations, performance metrics can beassigned based on expert human analysis or even explicit userresponses/input (e.g., similar circumstances where users reportsimilarly, etc.).

In one embodiment, the analysis generates one or more profiles. In oneexemplary embodiment, profiles can be continuously updated based oncrowd-sourced data from actual users. In other embodiments, the profilesmay be generated initially, but subsequent data analysis and profileupdates are triggered based on threshold events (e.g., based onexcessive deviation, etc.) Still other techniques may perform analysison a periodic basis, aperiodic basis, etc.

In one exemplary embodiment, the analysis may be subdivided into“stages” of analysis; for example, the analysis may need to identifywhich subsets of user workout and/or readiness/recovery data should begrouped together and/or what patterns and relationships exist in thesubsets of user workout and/or readiness/recovery data. In anotherexample, analysis may be iterative over time. For example, as useradoption increases and workout and/or readiness/recovery data continuesto accumulate, data analysis may be repeated so as to continuouslyimprove the accuracy and/or robustness of the profiles.

As used herein, the terms “analyze” and “analysis” refers to anytechnique, method, process, algorithm, and/or system that examines adata set to extract relationships from the data set. Analysis may bemanual (e.g., entry based on human analysis), automatic (machinelearning), or a hybrid thereof (e.g., software identification with humanacknowledgement, etc.) More generally, artisans of ordinary skill in therelated arts given the contents of the present disclosure, will readilyappreciate that virtually any scheme for data analysis may besubstituted with equal success the following being purely illustrative.

As a brief aside, humans are good at interpolating and/or extrapolatingrelationships in relatively small amounts of data. Traditional examplesof such relationships include, without limitation: predictiverelationships, causative relationships, correlative relationships,statistical behavior, probabilistic behavior, and/or any arithmeticallydefined function. However, human analysts are expensive to employ andare poorly suited for analyzing large, “noisy”, multi-variate data sets.Additionally, certain types of data analysis are “mining expeditions”that are commercially infeasible to pursue with human labor e.g., theresult may be too low yield, unpredictable, sparse, etc. Recently,however, advances in data science and machine learning have enabledautomated identification and recognition of patterns in data sets.Computer-based data analysis greatly expands the breadth and/or depth ofrelationships that can be identified in large data sets.

Machine learning algorithms vary widely in their approach, datarequirements, and tasks/capabilities. Machine learning typicallyinvolves creating a model, which is trained to process data to makepredictions therefrom. Common examples of machine learning modelsinclude e.g., artificial neural networks, decision trees, support vectormachines, Bayesian networks, genetic algorithms, and hybrids thereof.Common techniques for training include supervised learning, unsupervisedlearning, reinforcement learning, feature learning, sparse dictionarylearning, anomaly detection, multi-variate association, etc.

In one embodiment, different types of data may be analyzed withdifferent types of machine learning. For example, user classificationmay be based on supervised learning, whereas heuristic and performancemetric generation may be based on unsupervised learning. As a briefaside, supervised machine learning uses training data to inferrelationships where the input and output data types are known.Supervised learning models may be particularly suitable for e.g.,identifying which users should be grouped together under an expectedprofile because training data can include clear examples of enduranceathletes, sprint athletes, etc. In contrast, unsupervised machinelearning can be used to identify patterns in unstructured data (e.g.,where there is no clear definition of “input” or “output”). In otherwords, once the users for an expected profile have been identified,unsupervised learning may be used to “mine” for unknown characteristicsabout the users to generate heuristics and performance metrics for theexpected profile.

There may be a myriad of situations that require further derivation,inference, exploration, and/or feedback because the machine learnedrelationships are ambiguous or inaccurate. In such cases, it may not bepossible to classify the data with machine learning, without furtherhuman assistance (e.g., user input, expert analysis, etc.) For example,a user may need to provide additional feedback via explicit input, afitness test, sport evaluation, or otherwise provide proxy data (datarepresentative of an immeasurable quality) useful to support moreaccurate user profiling. In other cases, a physician or sports expertmay be able to identify intangible factors that lay outside of thecollected data.

In another such example, user classification can be continuouslyimproved based on new information using reinforcement learning. Moredirectly, once a user has been associated with an expected profile, theresulting actual workouts logged by the user can be used to furtherreinforce (or weaken) the strength of both the user's match and/or theexpected profile. In other words, the classification of a user shouldchange as the user's personal fitness journey changes. User progressionobviously benefits the user but may also be used to further tune thehealth tracking systems classification, coaching, and/or recommendationaccuracy.

Still other techniques for data analysis using machine learning may beadded, modified, substituted, parallelized, and/or sequentiallycascaded, by those of ordinary skill in the related arts, given thecontents of the present disclosure.

Referring back to step 402, various embodiments analyze workout and/orreadiness/recovery data to extract relevant patterns in physiologicaland/or psychological data. Common examples of workout data may include,without limitation: fitness goal definition, progression of performance,muscle group, duration of exertion, intensity of exertion, absoluteload, relative load, range of movement, repetition, recovery time,psychological parameters (e.g., behaviors, motivations, etc.), height,weight, age, sex, ethnicity, and/or any number of other user specificworkout parameters. Common examples of readiness and/or recovery datamay include, without limitation: date/time of sleep/consumption,suggested sleep time/consumable items, actual sleep time/consumption,contiguity of sleep, irregularity of sleep, quality of sleep, portionsize, time interval over which a consumable was consumed,nutrient-related information, and/or any number of other subjective userdata.

More generally, however, any parameter that affects user performance maybe considered. For instance, environmental conditions may affect humanphysiology and/or psychology. Such environmental factors may includee.g., elevation, temperature, humidity, sound (music), other people(workout buddies), season, and/or any number of other considerations. Asbut one example, a user that lives at sea level may perform differently(and thus require different coaching) when they are at a differentelevation.

Due to the sensitive nature of user data, various embodiments of thepresent disclosure may additionally anonymize certain information. Inone exemplary embodiment, the user may select what information may beanalyzed. In some variants, the user may be able to protect personallyidentifying information (name, height, weight, age, sex, etc.) In somevariants the user may be able to protect workout related information(e.g., time, location, duration, frequency of workout, etc.) As but oneexample of anonymization, identifying information may beunidirectionally hashed, scrambled, encrypted, and/or otherwiseobscured.

In alternative embodiments, user identifying information may be usede.g., to improve accuracy. For example, a first set of profiles may begenerated from anonymized user workout data for users that do not wantto release sensitive information and a second profile may be generatedfrom private user workout data for users that waive their privacy. Thesecond profile may offer additional benefits and/or better coachingbased on the improved specificity. For example, location and/ordemographics (age, ethnicity, sex) information may provide betterpsychographic profiling useful for niche motivational messaging.

In order to prevent malicious activity (e.g., polluting performancemetrics with misreporting, etc.), the user may be required to verifytheir authenticity in order to e.g., create user accounts and/orgenerate workout data. For instance, the user may be required to provideproof of existence (via e.g., an external email account, phone number,or other personal verification method). Additionally, in some cases, auser account may be validated to ensure that the user profileinformation is reasonable. For example, age, height and/or weight inputsmay be verified to lie within the realm of possibility. In some cases,external verification of personhood may require certification (trustcredentials) or other root of trust (e.g., trusted 3^(rd) partyverification, etc.) Still other techniques for managing user databaseintegrity may be substituted with equal success by artisans of ordinaryskill in the related arts.

As used herein, the term “workout” refers to one or more activitiesperformed by the user with measurable physiological and/or psychologicalimpact. Examples of measurable physiological impacts may include withoutlimitation e.g., cardiovascular strain, heart rate, caloric consumption,muscular exertion, fatigue, blood oxygenation, lactate production, bloodocclusion, nervous system activation, temperature increase, sweatproduction, changes to form/body positioning (via video analysis),audible data (exhalations, foot strikes, etc.), and/or any otherphysical effect of exertion. Physiological data may be collected via oneor more sensors and/or the user device interface (e.g., buttons, touchscreen, microphones, etc.). Common examples of sensors include e.g.,accelerometers, heart rate monitors, blood sensors, microphones,cameras, etc.

As used herein, “performance” and “performance metrics” refer to any setof workouts and/or predicted/expected physiological and/or psychologicalimpacts for similar users based on e.g., physiology, psychology, fitnessgoals and/or any other relevant parameters. As used herein, the term“performance progression” is used to refer to a user's tolerablephysiological and/or psychological impact as a function of time. Forexample, a user's physiological progression may be measured as afunction of e.g., changes to heart rate as a function of distance runover multiple workouts, changes to maximum repetitions/sets of a loadover multiple workouts, etc. Notably, while performance progression isgenerally measured physiologically, psychological measures may also havesignificant value. For example, some users may subjectively enjoyworking out regardless of whether or not they improve theirphysiological performance. Also, a user's psychological impact may causechanges to motivation and/or outlook when they hit a physiological“plateau.”

In one exemplary embodiment, heuristics refers to any logic that isconfigured to dynamically respond to user input. In one exemplaryembodiment, the heuristics are conditional rules for execution by aprocessor of the user device. As used herein, the term “conditional”refers to any action or event that is subject to one or more logicalconditions or requirements being met. Common examples of conditionalrules include e.g., “if-then”, “only-if-then”, “do-until”,“perform-while-true”, “case(s)”, and/or other Boolean logic. While theconditional logic is described herein, artisans of ordinary skill in therelated arts given the contents of the present disclosure will readilyappreciate that non-Boolean based rules may be substituted forconditional rules with equal success. Common examples of non-Booleanrules include without limitation: machine learned rules, pattern-basedrules, predictive rules, weighted rules, “fuzzy” logic, and/or othertechniques.

At step 404 of the method 400, the health tracking system recommendsworkouts to clients based on the expected profiles. In one embodiment,workout recommendations may be based on the expected profile and one ormore of e.g., user data records, workout data records, subjective userinput, sleep data records, consumable item data records, environmentalfactors, advertising, or other health tracking system processes. In oneexemplary embodiment, the expected profiles may be associated withheuristics and/or performance metrics that enable the client device toidentify salient events and/or provide dynamic feedback in view of therecommended workout. Each recommended workout may identify one or moreexercises, the expected performance corresponding thereto, and/or theconditions under which the user has deviated from expected performance.

For example, a client device may receive a workout that prescribes a setof exercises, an expected number of sets/repetitions, and/or rules foradjusting the workout if the user is underperforming due to injury,soreness, energy, mood, sleep, nutrition, or lack of motivation(“readiness adjustments”). In other embodiments, the health trackingsystem may push a number of workouts to a client device that aregenerally associated with the user's closest matching expected profile.When the user wants to do a workout (or schedule a workout for later),they can select one of the pushed workouts.

In one exemplary embodiment, the workout recommendations may be adjustedfor the user's readiness by the client device (readiness metrics may belocally stored for e.g., privacy, connectivity reasons, etc.) In onesuch variant, the workout recommendations may include heuristics thatdefine how the workout recommendations may be adjusted for the user'sreadiness. The heuristics may describe increasing or decreasing workoutintensity (e.g., sets, repetitions, duration, rest intervals, etc.)based on readiness metrics (described in greater detail infra).

In another exemplary embodiment, a set of workouts may be “pulled” by aclient device based on e.g., user parameters, the user's profile,previous workout history, or other client-side considerations. Forexample, a user may walk into their gym and identify equipment that isavailable (e.g., treadmills, kettlebells, and seated row) and/or theirtotal time for working out (e.g., 45 minutes), responsively the healthtracking system can identify workouts for the expected profile that bestmatch the user's specified criteria and readiness (soreness, energy,mood, sleep, nutrition, etc.) In another such case, a set of workoutsmay be pulled based on user input; for example, a user that has injuredthemselves (or cannot complete an assigned workout) may pull alternativeworkouts for their expected profile. Similarly, a user that has toadjust their workout intensity may need to get different workoutroutines mid-workout for their expected profile.

In some cases, a super set of workout suggestions may be pushed to auser and cached for internal retrieval. In one example, a user maygenerally receive a suggested set of routines that should be completedwithin e.g., a week. Any of the workouts can be locally queried,scheduled for activity, and/or logged at the client device withoutrequiring further health tracking system interaction. In anotherexample, a user may be “cusping” from one expected profile to another.The health tracking system may push suggested workouts that areassociated with both profiles. Thereafter, the client device may be ableto dynamically bridge between the suggested exercises during the user'sworkouts, thereby smoothly transitioning the user throughout theirpersonal fitness journey.

In some situations, a hybrid of “push” and “pull” may be used. Forexample, a user may pull a first set of workouts, and be pushed a secondset of workouts based on the expected profile; e.g., a user may requestupper body workouts, which additionally cause lower body workouts to bedelivered in tandem. In other cases, a user that requests a workout maybe suggested with alternative workouts so as to vary a user's workoutschedule. For example, a user that has explicitly selected a “20-minutewalk” may be pushed other workouts having similar effect (e.g., “mildcalisthenics”).

While the foregoing example is presented in the context of a healthtracking system and client device interaction, artisans of ordinaryskill in the related arts will readily appreciate that workouts may beprovided via alternative sources and/or avenues. For example, a clientdevice may send/receive workouts to/from other client devices and/orother parties; e.g., a smartphone that is paired to a smartwatch canprovide workouts thereto. Similarly, a user may be able to sendrecommended workouts to a training partner, etc. In some cases, usertransfers may occur in a broadcast or multicast manner; for example, acoach may send a team of athletes a set of workouts. Each athlete mayhave their own set of workouts further personalized based on theirspecific needs.

At step 406 of the method 400, the health tracking system updates theworkout data with logged performance. As used herein, “log” and/or“logging” refers to any addition, derivation, inference, and/orsubsequent manipulation of data records directed to user activity.Logging may be manual (e.g., entry by a user, personal trainer, coach,and/or other human), automatic (machine entry via e.g., client device,health tracking server, 3^(rd) party service, etc.), or a hybrid thereof(e.g., machine logged with human acknowledgement, etc.) More generally,artisans of ordinary skill in the related arts given the contents of thepresent disclosure, will readily appreciate that virtually any schemefor logging may be substituted with equal success, the following beingpurely illustrative.

In one embodiment, workout data records are received from the clientdevice and associated with the user profile. In one exemplaryembodiment, the workout data record includes e.g., time and/or date ofworkout, detailed information with regard to e.g., type and/or number ofexercises, exerted muscle groups, duration of exertion, intensity ofexertion, subjective user input, and/or any other workout specific data.Additionally, the user data record may include any salient events e.g.,where a heuristic was triggered and/or performance metric wasexceeded/not met by a prescribed range. Salient events and/or dynamicfeedback may include e.g., substitute exercises, added/removedexercises, completion status, duration, physiological and/orpsychological impact and/or other user input. For example, a user mayadditionally add personal notes and/or user tags (e.g., “Personal Best”,“Strained hamstring on 3^(rd) set”, etc.)

Dynamic feedback and/or readiness information for users may be animportant source of information for updating expected profiles.Empirical evidence suggests that while user subjectivity varies betweenindividuals, it is generally consistent over time for the sameindividual (e.g., a user that underreports discomfort is likely toconsistently underreport, etc.) Consequently, various exemplaryembodiments of the present disclosure may use subjective data (userinput) to further improve the heuristics and performance metricsassociated with expected profiles. In some cases, subjective reportingmay be difficult for non-human interpretation. Thus, some variants mayprovide anonymized user input to a human (e.g., a physical therapist,etc.) for interpretation, validation, verification, and/or translationinto objective metrics for updating expected profiles.

In other embodiments, dynamic feedback to workouts and/or readinessinformation may be specific to a user. For instance, the user may beable to “opt out” of providing subjective input, and their workoutand/or readiness information data records may be excluded from expectedprofile updates. Non-disclosed user input may detract from the accuracyand/or quality of a user's feedback. Validation and/or verification ofnon-disclosed user input may be unnecessary. Similarly, some variantsmay additionally allow for post-workout logging. In some cases, thepost-workout logging may be manual and/or user specific; in other cases,the post-workout logging may be automatic based on conditional rules.

In some embodiments, the user profile may be updated based on analysisof the user's workout, sleep, and/or consumable item data records. Forexample, a user that consistently falls below their personal health andfitness goals may receive encouragement and/or feedback to re-evaluatetheir goals. In some cases, user workout, sleep, and/or consumable itemdata records may be matched against expected goals to ensure thatadequate progress is being made. Logging is prone to error and/ormisreporting; e.g., some users may consistently under/overreport theirworkout, sleep, and/or nutritional regimen. Analyzing a user's ownperformance against the expected performance metrics of an expectedprofile may assist in user expectations and/or correct for misreporteddata. For example, a person that is consistently overreporting theirworkouts may show subpar performance gains. Under such situations, thehealth tracking system may further adjust the user's profile consistentwith a different expected profile and/or remind the user that trackingefficacy is based on diligent record keeping.

At step 408 of the method 400, the health tracking system recommendsrecovery programming to clients based on the expected profiles and thelogged workout performance. In one embodiment, recovery programmingrecommendations may be based on the expected profile and one or more ofe.g., workout data records, sleep data records, consumable item datarecords, environmental factors, advertising, or other health trackingsystem processes. The recovery programming may identify actionable itemsfor activities in the future. Common examples of actionable itemsinclude without limitation: sleep items (time and/or quality), nutritionitems (amount, macronutrients, timing, etc.) and/or heuristics forestimating variances in performance attributable to insufficientreadiness and/or recovery practice.

For example, a client device may receive a fitness program thatprescribes e.g., times for resting, meals for consumption, workoutsand/or rules for adjusting the program if the user is underperformingdue to lack of rest and/or nutrition. Thereafter, the client device canadjust the fitness program to emphasize sleep times if the user is notgetting enough rest or add/remove snacks or meals based on nutritionalrequirements.

In some cases, a super set of actionable items may be pushed to a user.As but one such example, a user may receive a calorically dense meal(for high caloric availability), a high protein meal (to assist inmuscle recovery), and a low-calorie meal. Thereafter, the fitnessprogram selects one of the meals based on the user's actual behavior(e.g., high performance, moderate performance, or skipping).

FIG. 4B is a logical flow diagram of an exemplary method 450 forcoaching a user with dynamic feedback, in accordance with the variousprinciples described herein.

At step 451 of the method 450, a client device obtains readinessmetrics. In one exemplary embodiment, the client device prompts the userfor subjective input regarding soreness, energy level, mood, and/orsleep. In another embodiment, the client device collects the readinessmetrics locally at the client device. In one exemplary embodiment, localcollection may include monitoring user activity and/or soliciting theuser for input. For example, a smart phone can track a user's day-to-dayactivities via application programming interface (API) integration withthe user's calendaring program.

In other embodiments, the client device may obtain readiness metricsfrom other devices of the user's community of personal devices. As usedherein, the term “personal device” refers to a set of devices associatedwith a user. The user's community of personal devices may have manydifferent functionalities that satisfy different useful niches withregard to personal health and fitness tracking. For example, a smartphone may enable a user to log meals and/or schedule their day-to-dayactivities. A smart watch may allow a user to capture movement, monitorheart rate, and/or track sleep. Still other variants will be readilyappreciated by artisans of ordinary skill in the related arts, theforegoing being purely illustrative.

In one exemplary embodiment, the client device may use hierarchicallogic to determine which readiness metrics should be used and/orpreferentially weighted from the community of personal devices. In somecases, the hierarchical weighting may be static (or set to a defaultconfiguration); for example, a smart shoe may be preferred for workoutdata, a smart watch may be preferred for sleep tracking, and a smartphone may be preferred for meal tracking. In other variants, thehierarchal weighting may be flexibly configured e.g., by the user,client device, and/or fitness tracking system. For example, a user mayprefer to use the step count provided by a smart watch, rather than asmart phone, etc. Similarly, the client device and/or fitness trackingsystem may preferentially prefer data from certain devices due to e.g.,manufacturing quality, business considerations, and/or other devicespecific considerations.

In some embodiments, the client device can provide the readiness metricsto a health tracking system to assist in workout recommendations and/orreadiness adjustments. In other embodiments, the client device can storethe readiness metrics (e.g., for privacy, connectivity reasons, etc.)and perform workout modifications locally.

Still other embodiments may provide a subset of readiness data. Forexample, the user may only wish to expose a certain subset of historicdata (e.g., the user may only wish to include sleep data, but not mealtracking data). In another such example, historic data may havediminishing value as a function of time; for example, user data beyond afew days may have little (or no) readiness value.

At step 452 of the method 450, the client device obtains workoutrecommendations and/or readiness adjustments from a health trackingsystem. In some cases, the client device may directly provide theworkout recommendations to the user. In other cases, the client devicemay consider other client-side considerations to further winnow downrecommended workouts. For example, a user may locally imposerestrictions (e.g., no workouts longer than 45 minutes, ignore workoutswhen outside of a geo-fenced “home gym” area, ignore workouts duringinjury/recovery periods, etc.)

In one embodiment, the workout recommendations and/or readinessadjustments may be received from a health tracking system based on theuser's profile and/or one or more closely matching expected profiles. Inother embodiments, the workout recommendations and readiness adjustmentsmay be further filtered by the client device based on client-sideinformation (e.g., user schedule, available equipment, user mood, etc.)e.g., to focus on the subset of workouts that the user is interested in.Still other embodiments may hybridize client-side and server-sideconsiderations. For example, the user may frequently pick their ownworkouts, while still being open to workouts recommended based onexpected profiles.

In some embodiments, the client device may receive workoutrecommendations and heuristics to locally perform readiness adjustments.As previously noted supra, the heuristics define how the workoutrecommendations may be adjusted for the user's readiness e.g.,increasing or decreasing workout intensity, dynamic coaching, etc. basedon readiness metrics. As but one such example, the client device mayprivately store the user's readiness metrics (e.g., sleep and/or mealtracking data); the client device can use the heuristics to modifyworkouts recommendations based on readiness metrics, without exposingsensitive user data. In another such example, the client device may haveintermittent connectivity (e.g., trail runs, extended backpacking, etc.)Local copies of the heuristics enable the client device to dynamicallymodify workouts recommendations using readiness data even in very remotelocations.

Once a workout is recommended, adjusted, and/or selected, the clientdevice monitors performance during the selected workout and providesdynamic feedback (if necessary) at steps 454 and 456 of the method 450.In one embodiment, the user may input workout data into the clientdevice and receive dynamic feedback via a visual user interface. In onesuch variant, the user interface may be a natively executed applicationrunning on a user's device (e.g., smart phone, watch, laptop, etc.).Other common embodiments may use a web browser, or other intermediaryweb portal located at home or at a gym. Users may use a screen,keyboard, and mouse or other computer peripherals to interact with thedisplayed workout (e.g., to see the various exercises, inputrepetitions/sets, etc.)

In other embodiments, the user may provide auditory input to the clientdevice and receive dynamic audible feedback therefrom; e.g., the clientdevice may read workout instructions aloud and/or accept voice commands.For example, the client device may instruct the user to start a push-upwith an audible “Down”; the user may respond with an audible “Up” toindicate the completion of a repetition. In a similar embodiment, haptictype interfaces may use accelerometers and/or haptic feedback tocommunicate with the user. For example, the client device may vibrate toinstruct the user to start a repetition, the user's motion (captured viaaccelerometer) may indicate completion of a repetition. Still othertypes of user interface may be substituted by artisans of ordinary skillin the related arts, given the contents of the present disclosure.

In one embodiment, the client device monitors for salient events and/orconditions where a heuristic was triggered. For example, the heuristiccan specify an expected performance metric within a prescribed range.Other common examples of heuristics may include without limitation e.g.,completion of an exercise too quickly/slowly, heart rate being toohigh/low, irregularity of form, excessive/minimal strain (via bloodocclusion, oxygenation, lactate, etc.), and/or any number of otherindicia.

As but one example, the heuristic may specify a number of repetitions;the client device counts repetitions, if the user cannot meet (orexceeds) the specified number of repetitions, then the client deviceprovides dynamic feedback and/or modification to the recommended workout(e.g., by increasing or reducing the next set's repetitions). In anothersuch example, the heuristic may specify an expected time of completion(e.g., each repetition/set should be completed within a time window),failure to remain within the time window may be indicative of fatigue orincorrect form. Still other examples may monitor a user's psychologicalstate; for example, a user that is tired early may be allowed to cuttheir workout short to prevent injury, or urged to complete the entireworkout, depending on the expected profile's psychometric rules.

In one embodiment, the heuristic and/or dynamic feedback may be based onthe readiness metrics. Consider a situation where the user has skipped ameal, the client device can infer that a dip in workout performance isdue to the caloric deficiency and may dynamically advise the user tohave a quick drink of a sports beverage (or similar calorie boost).Similarly, a user that is subjectively reporting that they have lowenergy may underperform; depending on the heuristic, the client devicecan adjust the workout intensity or provide dynamic motivation that ismore in line with the user's current energy level.

In some embodiments, the user may enable, disable, and/or postponedynamic feedback. For example, some users may find dynamic coachingdistracting during an exercise, but may be interested in receivingfeedback later (e.g., during a rest period, etc.) In thesecircumstances, the client device may track status, but only providefeedback when the user expressly wants to know how they did. In anothersuch example, some users may not want dynamic feedback in the form ofcoaching but are open to changes to the workout routine. In some suchvariants, the user may be able to see when and why the workout changed(if interested).

In some cases, dynamic feedback may additionally reference and/orpre-populate feedback based on pre-workout or post-workout activities.For example, a user that did not sleep well prior to exercise mayreceive informational messaging regarding the importance of sleep.Similarly, under performance that is attributable to a caloricdeficiency or dehydration may remind the user to eat a post-workoutsnack or drink. In some cases, multiple events may be referenced and/orpre-populated. For example, due to biological absorption constraints(e.g., 20-40 g of protein per hour), a hard workout may impact multiplesnacks/meals. A 2-hour high intensity workout may call for apost-workout shake with 20-40 grams of protein and, 60-90 min later, aprotein-augmented lunch (“eat an additional 20-grams of protein atlunch”). Similar considerations may apply for sleep, etc.

In still other embodiments, a user may be able to provide their user toanother user. For example, a user may want to provide their dynamicfeedback to a personal trainer. In this manner, the user may benefitfrom personal trainer advice without paying the personal trainer towatch them exercise. Similarly, the personal trainer may be able toremotely coach a much larger clientele.

In one embodiment, subjective user reporting is incorporated as part ofthe dynamic coaching experience throughout the workout; for example, auser may be prompted to provide e.g., rate of perceived effort (RPE)assessments throughout the workout. The RPE input may be used to adjustworkout intensity. In some variants, RPE reporting is triggered based onoverperformance or underperformance relative to the recommended workout.For example, a user that has failed to meet their prescribed number ofrepetitions is prompted to report their RPE; the RPE can be used inconjunction with the user's expected profile to provide dynamic feedbackand/or workout modification. In still other variants, RPE reporting maybe provided at the end and incorporated with the workout data recordsfor post-workout analysis.

In some embodiments, the client device may initiate subjective userinput reporting based on e.g., the user's expected profile. In otherembodiments, subjective user input reporting may be configured by a3^(rd) party. For example, a coach can insert user reporting events fora coached athlete to ensure that the athlete remains on track. Othercommon examples of 3^(rd) parties that may be appropriately permissionedto request user input include without limitation: teammates, personaltrainers, and/or training partners.

The user's subjective input may be used to improve future workoutrecommendations. For example, a person that routinely underperforms dueto lack of readiness or failure to maximize recovery can be shifted to aslower progression track. In another such example, user input can beused to shift workout scheduling e.g., to ensure that the user isadequately rested and/or fed before and after workouts.

Various other schemes for monitoring a user's progress in view of anexpected profile will be readily appreciated by artisans of ordinaryskill, given the contents of the present disclosure.

At step 458 of the method 450, the client device logs the user's actualworkout data. In some embodiments, the user may be prompted to reconcilemonitored exercise with the actual exercise performed. For example, auser may have been recommended an exercise, but decided to do somethingelse (due to injury, equipment availability, mood, etc.) In otherexamples, the client device's sensors may be unable to accurately gaugecompletion, inaccurate, and/or faulty. The actual workout data recordsshould only reflect what the user completed (or failed to complete).Actual user workout data may be stored as user workout data records,which may be stored at either or both of the client device and/or thehealth tracking system.

In some embodiments, a user may be able to locally access their userworkout data records. In some cases, the user's immediate access toprevious user workout data records may be useful to e.g., trackprogress, plan for future workouts, and/or used for other motivationalpurposes. In some cases, the user workout data records can be madeaccessible via e.g., external application programming interfaces (APIs)to a variety of other tools.

At step 459 of the method 450, the user's schedule is updated withrecovery programming. In one embodiment, the recovery events may beinformative messaging and/or reminders (e.g., “good job, remember torest and refuel”, etc.) In some embodiments, the recovery events mayinclude actionable items. For example, recovery programming mightinclude actionable items for e.g., sleep and a snack.

In one exemplary embodiment, actionable events may include any of e.g.,time and/or date, detailed requirements with regard to the actionableevent (e.g., required sleep duration, required sleep quality,macronutrient requirements, hydration amounts, etc.), informationalmessaging, acceptable substitutions, and/or any other recovery specificdata. For example, a user that has completed a particularly strenuousexercise may have sweated more than expected; as a result, the user isprovided with an actionable event to rehydrate (e.g., drink 16 oz ofwater, within an hour). In another such example, a sleep deprived userthat did not meet their expected performance may be provided with anactionable event to get to sleep earlier; similarly, a user that skippeda meal before running may be prescribed a more substantial post-workoutrefuel snack (or in some cases, multiple snacks/meals).

In some embodiments, the recovery programming may be adjusted for theuser's particular requirements by the client device (based onclient-side considerations, etc.) In one such variant, the recoveryprogramming may include heuristics that define how the recoveryprogramming recommendations may be adjusted for the user'sconsiderations. For example, heuristics may describe substitutionsbetween different macronutrients (e.g., protein for fat orcarbohydrates, and vice versa). In another such example, the heuristicsmay provide the user with different options e.g., high protein formuscle recovery, high fat for sustained energy, etc.

Additionally, as previously alluded to, recovery habits may haveshort-term and/or long-term effects. Consequently, recovery items mayonly be valid during a specified time window. In such cases, recoveryprogramming may expire (a user that cannot refuel within a certain timewindow may miss out on optimal performance regardless of subsequentconsumption).

Other common examples of client-side considerations may include withoutlimitation: available time and/or resources, other scheduled tasks,subjective user input, convenience, business considerations, etc. Forexample, a user may receive an eight-hour sleep action item; the sleepitem may be flexibly scheduled based on the user's existing schedule(e.g., a bedtime reminder is calculated based on a client-side alarmsetting). Similarly, a user may receive a suggested meal item but needto modify the ingredients based on e.g., what they currently haveavailable in their pantry. As but another such case, informationalmessaging may be tied to promotional offers (e.g., “to get the most outof your workout, grab a protein shake at the gym.”)

In some embodiments, actionable items may include subsequent reportingback to the health tracking system. Subsequent reporting may includedata regarding the user's subsequent activity (e.g., completion, percentadherence, skipped, etc.) For example, a user that is prescribed apost-workout snack and rest may be assessed for adherence to thesuggestions. In some cases, adherence data can be useful for updatingthe user's profile.

More generally, artisans of ordinary skill in the related arts willreadily appreciate that a wide expanse of usability may be necessarywith individualized user workouts and/or readiness/recovery programming.In an ideal world, every workout can be perfectly logged, yet real worldconsiderations exist. While accuracy is important, “perfect” adherenceis not required for user benefit; “good enough” may suffice to keepusers on their personal health and fitness journey.

While the foregoing examples are presented in the context of a singleworkout, artisans of ordinary skill in the related arts, given thecontents of the present disclosure, will readily appreciate that aworkout regimen can be steered over time so as to enable a user toachieve particular fitness goals. As but one example, a casual athleteuser (e.g., classified in a first expected profile) might have a goal toimprove their sport performance in view of a sports idol (e.g., a secondexpected profile.) The most realistic way for the casual athlete user toachieve their goals may be greatly affected by their physiological andpsychological characteristics. In some cases, similarly situated andmotivated users may have greater success by e.g., focusing onphysiological goals (e.g., working on one muscle group before another,focusing on flexibility, etc.) and/or psychological goals (e.g.,establishing a regular workout discipline, pushing through discomfort,etc.) In other words, steering users to improve and transition to newexpected profiles may be more efficient to achieve performanceprogression than others.

Apparatus

FIG. 5A is a logical block diagram of one exemplary server apparatus500, useful in accordance with the various principles described herein.In one embodiment, the server apparatus 500 includes a processor 502,non-transitory computer-readable medium 504, and one or more networkinterfaces (e.g., a first network interface 506, and a second networkinterface 508).

The components of the exemplary server apparatus 500 are typicallyprovided in a housing, cabinet or the like that is configured in atypical manner for a server or related computing device. It isappreciated that the embodiment of the server 500 shown in FIG. 5A isonly one exemplary embodiment of a server 500 for the health trackingsystem. As such, the exemplary embodiment of the server 500 describedherein with reference to FIG. 5A is merely representative of any ofvarious manners or configurations of servers or other data processingsystems that are operative in the manner set forth herein.

The processing circuitry/logic 502 of the server 500 is operative,configured, and/or adapted to operate the server 500 including thefeatures, functionality, characteristics and/or the like as describedherein. To this end, the processing circuit 502 is operably connected toall of the elements of the server 500 described below.

The processing circuitry/logic 502 of the host server is typicallycontrolled by the program instructions contained within the memory 504.The program instructions 504 include a workout coaching application thatenables dynamic feedback as explained in further detail supra. Theworkout coaching application at the server 500 is configured tocommunicate with and exchange data with the client-side workout coachingapplication running on a processor of the health tracking devices. Inaddition to storing the instructions 504, the memory 504 may also storedata for use by the health tracking program. As previously described,the data may include the user data records, the user workout datarecords, sleep tracking data records, meal tracking data records, and/orexpected profiles.

The network interfaces of the server 500 allows for communication withany of various devices using various means. In one particularembodiment, the network interface is bifurcated into a first networkinterface 506 for communicating with other server apparatuses and asecond network interface 508 for communicating with user devices. Otherimplementations may combine these functionalities into a single networkinterface, the foregoing being purely illustrative.

In one exemplary embodiment, the first network interface 506 is a widearea network port that allows for communications with remote computersover the Internet (e.g., external databases). The first networkinterface 506 may further include a local area network port that enablescommunication with any of various local computers housed in the same ornearby facility. In at least one embodiment, the local area network portis equipped with a Wi-Fi transceiver or other wireless communicationsdevice. Accordingly, it will be appreciated that communications with theserver 500 may occur via wired communications or via the wirelesscommunications. Communications may be accomplished using any of variousknown communications protocols.

In one exemplary embodiment, the second network interface 508 is anetwork port that allows for communications with a population of healthtracking user devices. The second network interface 508 may beconfigured to interface to a variety of different networkingtechnologies consistent with consumer electronics. For example, thenetwork port may communicate with a Wi-Fi network, cellular network,and/or Bluetooth devices.

In one exemplary embodiment, the server 500 is specifically configuredto analyze workout data records and/or readiness/recovery informationfrom a number of users to generate expected profiles that describe e.g.,the physiological and/or psychological behavior of similar users interms of heuristics and performance metrics in accordance with theprinciples described above. In particular, the illustrated serverapparatus 500 stores one or more computer-readable instructions thatwhen executed enable e.g., analyze workout data records and/orreadiness/recovery information from a population of users to generateprofiles, recommend workouts to users based on the profiles and/orreadiness metrics/heuristics, update workout data records with loggedperformance, and update user scheduling based on recovery programming.

FIG. 5B is a logical block diagram of one exemplary user apparatus 550,useful in accordance with the various principles described herein. Inone embodiment, the exemplary user apparatus 550 includes a processor552, non-transitory computer-readable medium 554, a network interface556, a user interface 558, and sensors 560.

In one exemplary embodiment, the user devices 550 are configured tomonitor a user's workout progress and/or readiness/recovery metrics anddynamically provide feedback in accordance with an expected profile.User devices 550 may also be referred to herein as health and/oractivity monitoring devices, or client devices. The user devices 550, inone exemplary implementation, include one or more portable computerizeddevices that are configured to e.g., recommend, display, monitor,feedback, motivate, and/or otherwise provide workout information to auser. In an exemplary embodiment, the specific workout information thatare displayed may include the exercise, current progress, expectedprogress, and/or any dynamic feedback associated therewith.

In one exemplary embodiment, the user devices 550 are additionallyconfigured to enable a user to log actual workout activity. The userdevices 550 may include one or more portable computerized devices thatare configured to measure, obtain, monitor, generate, collect, sense, orotherwise receive physiological and/or psychological impact experiencedby a user. In an exemplary embodiment, the specific data that arecollected may include e.g., repetition count, set count, duration, aswell as physiological information such as e.g., heart rate, bloodoxygenation, carbon dioxide production, lactate production, bloodocclusion, nervous system activation, sweat, blood sugar, etc.

In some embodiments, the user devices 550 may include a variety ofsensors 560 including, without limitation: accelerometers, heart ratemonitors, cameras, microphones and/or other sensing mechanisms (e.g.,blood monitors, etc.) For example, a client device may use anaccelerometer to e.g., count steps, repetitions, monitor motion (e.g,form). Similarly, blood oxygen sensors can detect e.g., bloodoxygenation, lactate production, etc. Cameras can be used to monitorchanges to form/body positioning (via video analysis), and microphonescan be used to capture audible data (exhalations, foot strikes, etc.)

In one variant, certain ones of the user devices 550 may includewearable health-related parameter measurement and computing devices,such as e.g., a smart watch, an activity tracker, a heart rate monitor,a sleep tracking device, a smart scale, and/or smart eyeglasses. Inaddition, an exemplary user device 550 may include a smartphone havingone or more of the foregoing capabilities and/or which enables userentry of the foregoing workout data. Alternatively, the user device 550may be in communication with a health and/or activity monitoring device.

Other examples of health parameter data may include data that theparticular device 550 is configured to collect (such as athleticactivity, biometric information, and environmental data). For example,an activity tracking device may be configured to collect activity datasuch as steps taken, distance traveled, rate or pace of a run, and/orflights of stairs climbed, etc.; a heart rate monitor may be configuredto collect heartbeat data; a sleep tracking device collects datarelating to how much time a user/wearer spends sleeping; a nutritiontracking device collects data relating to food and drinks consumed by auser; a smart scale collects data relating to a body weight, body fatpercentage, and/or body mass index (BMI), etc. Furthermore, a smartwatchand/or smartphone, may be utilized as an activity tracking device, aheart rate monitor, a sleep tracking device, and/or a nutrition trackingdevice. The user device 550 may comprise any of the foregoing types ofdevices and/or may receive collected data from a first device at one ormore applications running on the user device 550.

The exemplary user device 550 may be further configured enable entryand/or display of collected data. In such instances, the exemplary userdevice 550 may run one or more applications configured to process (e.g.,transform) the collected data. Exemplary applications include e.g., UARecord™, MapMyFitness®, MyFitnessPal®, Endomondo®, etc. each owned bythe Assignee hereof. Other health activity related monitoringapplications may additionally be utilized in connection with the presentdisclosure, such as those specifically designed to receive informationfrom a particular type of health monitoring device (e.g., a 1^(st) partyapplication which is published by the device manufacturer, or 2^(nd)party (trusted) or 3^(rd) party (untrusted) applications designed towork in conjunction therewith); the foregoing being merelyrepresentative of the general concepts of the present disclosure.

Additionally, in one exemplary embodiment the application(s) running atthe user device 550 includes a workout coaching application that enablesdynamic feedback in accordance with an expected profile and/orreadiness/recovery information. The workout coaching application enablesa user to receive workout recommendations and/or log workout activity.As discussed in greater detail supra, the workout coaching applicationenables a user to manage their workout regimen via communication toand/or coordination with a network side application run at the healthtracking server 500.

In one exemplary embodiment, the exemplary user device 550 isspecifically configured to provide dynamic feedback based on an expectedprofile and/or readiness/recovery information in accordance with theprinciples described above. In particular, the illustrated user device550 stores one or more computer-readable instructions that when executedare configured to cause the user device 550 to e.g., obtain readinessmetrics, obtain workout recommendations and/or readiness adjustmentsfrom a health tracking system, monitor performance during the selectedworkout and provide dynamic feedback (if necessary), log the user'sactual workout data, and update the user's schedule with recoveryprogramming

The above described system and method solves a technological problemcommon in industry practice related to individually tailoring workoutrecommendations for individual users with different physiological and/orpsychological traits. The above-described system and method improves thefunctioning of the computer/device by recommending workouts to the userbased on collecting workout data and/or readiness/recovery informationfrom a population of similarly situated individuals and ensuring thatthe user can receive dynamic feedback in accordance with an expectedperformance.

Portions of the system and methods described herein may be implementedusing one or more programs or suitable software code, such as theworkout recommendation application on the health tracking device and thehealth tracking program on the server, both described above, each ofwhich may reside within the memory of the respective computing devicesas software or firmware. Such programs and code may be stored in thememory and executed by the processor of the display device or a systemserver or other computer in communication with the display device. Acomputer program product implementing an embodiment disclosed herein maytherefore comprise one or more computer-readable storage media storingcomputer instructions translatable by processing circuitry/logic, a CPU,or other data processing device to provide an embodiment of a system orperform an embodiment of a method disclosed herein. Computerinstructions may be provided by lines of code in any of variouslanguages as will be recognized by those of ordinary skill in the art.

A “computer-readable medium” may be any type of data storage medium thatcan store computer instructions and/or data, including, read-only memory(ROM), random access memory (RAM), hard disks (HD), data cartridges,data backup magnetic tapes, floppy diskettes, flash memory, optical datastorage, CD-ROMs, or the like. The computer-readable medium can be, byway of example, only but not by limitation, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,system, device, or computer memory. The computer-readable medium mayinclude multiple computer-readable media storing computer executableinstructions, such as in a distributed system or instructions storedacross an array. A “non-transient computer-readable medium” may be anytype of data storage medium that can store computer instructions,including, but not limited to the memory devices discussed above.

FIGS. 6A-6D depict graphical representations of an exemplary userinterface, consistent with the various principles described herein.

FIG. 6A illustrates a user interface for requesting user input regardingtheir personal fitness goals during onboarding. The user input can beused for initial assessment, baselining, and profile construction. Insome cases, the information enables the health and fitness trackingsystem to tailor content and target products that users are interestedin.

FIG. 6B illustrates a user interface which identifies user parametersdetermined during an initial assessment/baseline and/or subsequentexercises. The illustrated embodiment shows an exercise and relevantmeasured performance metrics (e.g., performance per muscle group, etc.)

FIG. 6C illustrates a user interface which provides a program view thatenables a user to manage their day-to-day personal fitness journey. Inthe illustrated embodiment, the program view provides a schedule ofdaily tasks to complete in order for users to stay on track, avoidinjury, and succeed in their personal goals. The program view provides acomplete training plan that incorporates workouts, nutrition, and sleep.In one exemplary embodiment, the program includes actionable steps(e.g., meals to eat, times to sleep, workouts to complete, etc.) thatare automatically populated and/or manually re-configurable in view ofthe user's scheduling constraints (e.g., personal and professionalappointments). The actionable steps are generated based on the expectedprofile that is associated with the user.

FIG. 6D illustrates a user interface which dynamically generates aworkout based on e.g., physiological and/or psychological user input.Specifically, a workout is generated based on readiness inputs, currentlocation, and workout schedule. The workout intensity may be modifiedbased on readiness inputs and past rate of perceived effort (RPE)inputs. The workout length may be adjusted to complete before upcomingevents in the user's calendar. Location information may be used torecommend exercises based on the available equipment at the workoutlocation.

FIG. 6E illustrates a user interface which recommends and providesdynamic feedback to users during their workout regimen. The interfaceprovides informative references for the exercise, and visual indicationon routine progress. During the workout, the user is prompted to provideRate of Perceived Effort (RPE) feedback along the way. RPE can be usedto provide dynamic feedback as well as improve future workoutrecommendations.

FIG. 6F illustrates updates to the aforementioned program view based onthe user's activity. In the illustrated embodiment, the program view hasupdated the user's daily tasks with information regarding the user'sactual behavior. For example, as shown in FIG. 6F, the user's sleepquality is provided (e.g., 7hr 49min) along with qualitativemeasurements that were gathered from the user's sleep tracking devices.Similarly, the user's workout activity is updated to reflect caloricand/or hydration deficits as a function of time; this data can be usedfor updating (increasing/decreasing if necessary) post-workout refuelsnack/meal suggestions.

In the foregoing description, various operations may be described asmultiple discrete actions or operations in turn, in a manner that may behelpful in understanding the claimed subject matter. However, the orderof description should not be construed as to imply that these operationsare necessarily order dependent. In particular, these operations may notbe performed in the order of presentation. Operations described may beperformed in a different order than the described embodiment. Variousadditional operations may be performed and/or described operations maybe omitted in additional embodiments.

The foregoing detailed description of one or more exemplary embodimentsof the health tracking system with workout coaching has been presentedherein by way of example only and not limitation. It will be recognizedthat there are advantages to certain individual features and functionsdescribed herein that may be obtained without incorporating otherfeatures and functions described herein. Moreover, it will be recognizedthat various alternatives, modifications, variations, or improvements ofthe above-disclosed exemplary embodiments and other features andfunctions, or alternatives thereof, may be desirably combined into manyother different embodiments, systems or applications. Presentlyunforeseen or unanticipated alternatives, modifications, variations, orimprovements therein may be subsequently made by those skilled in theart which are also intended to be encompassed by the appended claims.Therefore, the spirit and scope of any appended claims should not belimited to the description of the exemplary embodiments containedherein.

In another embodiment, a permanent copy of the programming instructionsfor individual ones of the aforementioned applications may be placedinto permanent storage devices (such as e.g., memory) during manufacturethereof, or in the field, through e.g., a distribution medium (notshown), such as a compact disc (CD), or through communication interface(from a distribution server). That is, one or more distribution mediahaving an implementation of the agent program may be employed todistribute the agent and program various computing devices.

It will be appreciated that the various ones of the foregoing aspects ofthe present disclosure, or any parts or functions thereof, may beimplemented using hardware, software, firmware, tangible, andnon-transitory computer-readable or computer usable storage media havinginstructions stored thereon, or a combination thereof, and may beimplemented in one or more computer systems.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the disclosed embodiments ofthe disclosed device and associated methods without departing from thespirit or scope of the disclosure. Thus, it is intended that the presentdisclosure covers the modifications and variations of the embodimentsdisclosed above provided that the modifications and variations comewithin the scope of any claims and their equivalents.

1. A method for dynamically coaching a user based on one or morereadiness metrics, comprising: obtaining the one or more readinessmetrics from a first device of a user's personal community of devices;obtaining a recommended workout and a profile from a server, where therecommended workout comprises a plurality of exercises and the profilecomprises a performance metric; adjusting the performance metric priorto the recommended workout based on the one or more readiness metrics;monitoring a performance during the recommended workout at a seconddevice of the user's personal community of devices, the second deviceconfigured to collect physiological data; and when the performance doesnot match the adjusted performance metric, modifying at least oneexercise of the recommended workout based on the profile.
 2. The methodof claim 1, wherein the one or more readiness metrics comprises anamount or quality of sleep measured by the first device.
 3. The methodof claim 1, wherein the one or more readiness metrics comprises anestimated caloric availability estimated by the first device.
 4. Themethod of claim 1, wherein the one or more readiness metrics comprises asubjective input provided by the user via the first device.
 5. Themethod of claim 1, further comprising adjusting the recommended workoutbased on a shortened available duration.
 6. The method of claim 1,further comprising providing dynamic feedback based on the one or morereadiness metrics.
 7. The method of claim 1, further comprising causinga workout data record to be updated at the server based on the one ormore readiness metrics.
 8. A method for providing a user with recoverycoaching, comprising: obtaining a recommended workout and a profilecomprising a performance metric; monitoring a performance during therecommended workout via a first device of a user's personal community ofdevices configured to collect physiological data; when the performancedoes not match the performance metric, providing dynamic feedback; andupdating a scheduled event within a calendaring program based on theperformance monitored at the first device and a readiness metric or arecovery metric obtained from a second device of the user's personalcommunity of devices.
 9. The method of claim 8, wherein the scheduledevent comprises a suggested rest event.
 10. The method of claim 9,wherein the updating comprises modifying the suggested rest event; andwherein the modified suggested rest event is based on the performance.11. The method of claim 8, wherein the scheduled event comprises asuggested post-workout consumption event; wherein the updating comprisesmodifying the suggested post-workout consumption event; and wherein themodified suggested post-workout consumption event is based on theperformance.
 12. The method of claim 8, wherein the updating comprisesadding a suggested rest event or a suggested post-workout consumptionevent.
 13. The method of claim 12, further comprising creating a datarecord based on a completion status of the suggested rest event or thesuggested post-workout consumption event.
 14. The method of claim 13,further comprising updating the profile based on at least the datarecord.
 15. A user apparatus, comprising: a sensor configured to collectphysiological data; a user interface; a network interface configured tocommunicate with a community of user devices; a processor; and anon-transitory computer-readable medium comprising one or moreinstructions, which when executed by the processor, causes the userapparatus to: collect at least one of a readiness metric or a recoverymetric via the community of user devices; obtain a recommended workoutcomprising a plurality of exercises and a profile comprising aperformance metric; monitor performance during the recommended workoutbased on the physiological data; and modify at least one exercise of therecommended workout based on the profile and the at least one of thereadiness metric or the recovery metric.
 16. The user apparatus of claim15, wherein at least one device of the community of user devicescomprises a sleep tracking sensor; and wherein the readiness metriccomprises an amount or quality of sleep measured before the recommendedworkout.
 17. The user apparatus of claim 15, wherein at least one deviceof the community of user devices comprises a sleep tracking sensor; andwherein the recovery metric comprises an actual amount or quality ofsleep measured after the recommended workout.
 18. The user apparatus ofclaim 15, wherein the one or more instructions, when executed by theprocessor, further causes the user apparatus to: log one or moreconsumption events; and estimate the readiness metric based on the oneor more consumption events before the recommended workout.
 19. The userapparatus of claim 15, wherein the one or more instructions, whenexecuted by the processor, further causes the user apparatus to: log oneor more consumption events; and estimate the recovery metric based onthe one or more consumption events after the recommended workout. 20.The user apparatus of claim 15, wherein the one or more instructions,when executed by the processor, further causes the user apparatus to:solicit subjective user input prior to the recommended workout; andestimate the readiness metric based on the subjective user input.